Citrus Diseases Detection: Project Overview

This tool identifies diseased citrus trees by classifying citrus leaf images based on disease type.

• Leverages the citrus_leaves dataset from TensorFlow Datasets.
• Performs data preprocessing for standardization and label encoding.
• Builds and evaluates a Convolutional Neural Network (CNN) on the dataset.
• Develops a user-friendly client-facing API using Flask.

Code and Resources Used

Python Version: 3.10
Packages: numpy, pandas, matplotlib, tensorflow, tensorflow_datasets, flask, pillow
Flask API Setup:

• pip install -r requirements.txt
• conda env create -n <ENVNAME> -f environment.yaml (Anaconda Environment)

Dataset: https://www.tensorflow.org/datasets/catalog/citrus_leaves?hl=en

Getting Data

The project utilizes the citrus_leaves dataset from TensorFlow Datasets, containing 594 PNG images of citrus leaves categorized into four labels: Black Spot, Canker, Greening, and Healthy. The images have a resolution of 256x256 pixels.

Data Preprocessing

• Data Split: Divides the dataset into 80% training and 20% testing data for robust model generalization.
• Image Preprocessing: Reshapes and normalizes the images to a standard format and pixel range.
• Label Encoding: Converts labels to one-hot encoded format for efficient processing and multi-label handling.

Model Building

Constructs a Convolutional Neural Network (CNN) with the following architecture:

Model Evaluation

Measures the model's loss using categorical cross-entropy and optimizes it with the ADAM algorithm. Achieved the following results:

Productionization

Develops a user-friendly UI using Flask. The API endpoint receives image requests and returns predicted citrus disease types for each image.